Analysis of solvent vapors in breath and ambient air with a surface acoustic wave sensor array

Abstract

This article describes the development and evaluation of a small prototype instrument employing an array of four polymer-coated surface acoustic wave (SAW) sensors for rapid analysis of organic solvent vapors in exhaled breath and ambient air. A thermally desorbed adsorbent preconcentrator within the instrument is used to increase sensitivity and compensate for background water vapor. Calibrations were performed for breath and dry nitrogen samples in Tedlar bags spiked with 16 individual solvents and selected binary mixtures. Responses were linear over the 50- to 400-fold concentration ranges examined and mixture responses were additive. The resulting library of vapor calibration response patterns was used with extended disjoint principal components regression and a probabilistic artificial neural network to develop vapor-recognition algorithms. In a subsequent analysis of an independent data set all individual vapors and most binary mixture components were correctly identified and were quantified to within 25% of their actual concentrations. Limits of detection for a 0.25 l. sample collected over a 2.5-min period were <0.3×TLV for 14 of the 16 vapors based on the criterion that all four sensors show a detectable response. Results demonstrate the feasibility of this technology for workplace analysis of breath and ambient air.